CamoNAS applies neural architecture search with an RGB-frequency dual-stream design to reach state-of-the-art results on four camouflaged object detection benchmarks.
arXiv preprint arXiv:2009.01027 , year=
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DPOP is a new loss function that prevents DPO from lowering preferred response likelihoods and outperforms standard DPO on diverse datasets, MT-Bench, and enables Smaug-72B to exceed 80% on the Open LLM Leaderboard.
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CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection
CamoNAS applies neural architecture search with an RGB-frequency dual-stream design to reach state-of-the-art results on four camouflaged object detection benchmarks.
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Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive
DPOP is a new loss function that prevents DPO from lowering preferred response likelihoods and outperforms standard DPO on diverse datasets, MT-Bench, and enables Smaug-72B to exceed 80% on the Open LLM Leaderboard.